DATA1901

Project 1

Executive Summary

This report examines pre-VR conditions and side effect severity. Analyses include a scatter plot showing a positive relation between VR anxiety and severity, double box plots comparing state anxiety and severity, and a comparative box plot linking expectancy to severity. Findings suggest anxiety and expectancy positively relate to side effects.

Exploratory Data Analysis (EDA)

Structures

The data for this project comes from Saunders et al.’s 2023 research report. It includes data from 336 participants and 82 different variables.

Quantitative Variables

  1. p_vra (Discrete) : VR anxiety from 0 to 10.
  2. expect (Discrete) : Expectancy score.
  3. ssq_full (Discrete) : Side effect severity calculated from ASSQ minus BSSQ.

Qualitative Variables

The following below variables represent active state anxiety index on a 4-point Likert scale (“Very Much”, “Moderate”, “Somewhat”, “Not at all”), which is an ordinal type.

  1. PSTAI1 : Calm
  2. PSTAI2 : Tense
  3. PSTAI4 : Relaxed
  4. PSTAI6 : Worried

Limitations

The study was single-blind, which may have caused observer bias. It was conducted via Zoom, so results depended on participants’ internet connection. Data were collected in 2021 during the COVID-19 pandemic, which may have influenced side effects.

Assumptions

We assumed the variables (PSTAI, p_e, and p_vra) accurately measure anxiety and that participants understood and completed the survey correctly.

Data Cleaning & Transformation

PSTAI responses were aggregated into positive (1: “Very much”, “Somewhat”, “Moderately”) and negative (0: “Not at all”). Expectancy was split into Low/High by the median. These transformations are for easier analysis in identifying anxiety and non-anxiety groups.

Code
pooled_binary <- pooled_data %>%
  mutate(
    PSTAI_1_bin = case_when(
      PSTAI_1 %in% c("Very Much", "Moderately", "Somewhat") ~ 1,
      PSTAI_1 == "Not at all" ~ 0,
      TRUE ~ NA_real_
    ),
    PSTAI_2_bin = case_when(
      PSTAI_2 %in% c("Very Much", "Moderately", "Somewhat") ~ 1,
      PSTAI_2 == "Not at all" ~ 0,
      TRUE ~ NA_real_
    ),
    PSTAI_4_bin = case_when(
      PSTAI_4 %in% c("Very Much", "Moderately", "Somewhat") ~ 1,
      PSTAI_4 == "Not at all" ~ 0,
      TRUE ~ NA_real_
    ),
    PSTAI_6_bin = case_when(
      PSTAI_6 %in% c("Very Much", "Moderately", "Somewhat") ~ 1,
      PSTAI_6 == "Not at all" ~ 0,
      TRUE ~ NA_real_
    )
  )

pooled_melt <- melt(pooled_binary,
                    id.vars = "ssq_full", 
                    measure.vars = c("PSTAI_1_bin", "PSTAI_2_bin", "PSTAI_4_bin", "PSTAI_6_bin"),
                    variable.name = "PSTAI_var",
                    value.name = "Binary")

pooled_melt <- pooled_melt %>%
  mutate(Label = ifelse(PSTAI_var == "PSTAI_1_bin", "Calm",
                 ifelse(PSTAI_var == "PSTAI_2_bin", "Tension",
                 ifelse(PSTAI_var == "PSTAI_4_bin", "Relaxed",
                 ifelse(PSTAI_var == "PSTAI_6_bin", "Worry", NA)))),
         Label = factor(Label, levels = c("Calm", "Relaxed", "Tension", "Worry")),
         Binary = factor(Binary, levels = c(0,1), labels = c("Negative","Positive")))

median_expect <- median(pooled_data$expect, na.rm = TRUE)
pooled_data$expect_group <- ifelse(pooled_data$expect <= median_expect, "Low", "High")

Research Question and Analysis

Research Question

RQ1 : Is there an association between anxiety before undergoing VR and side effect severity after VR exposure?

Analysis

Code
cor(pooled_data$p_vra, pooled_data$ssq_full)
[1] 0.3877125
Code
figure_1 <- ggplot(pooled_data, aes(x = p_vra, y = ssq_full)) +
    geom_point() +
    geom_smooth(method = "lm", formula = y ~ x, se = FALSE) +
    labs(title = "Side Effect Severity vs VR Anxiety",
         x = "VR Anxiety",
         y = "Side Effect Severity")

ggplotly(figure_1)
Code
model = lm(ssq_full ~ p_vra, data = pooled_data)
figure_2 <- ggplot(model, aes(x = .fitted, y = .resid)) + 
  geom_point() + 
  geom_hline(yintercept = 0, linetype = "dashed", colour = "red") +
  labs(title = "Residual Plot")

ggplotly(figure_2)
Code
summary(model)

Call:
lm(formula = ssq_full ~ p_vra, data = pooled_data)

Residuals:
    Min      1Q  Median      3Q     Max 
-32.977 -10.385  -3.590   7.204  86.410 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   3.7997     1.4447   2.630  0.00893 ** 
p_vra         2.5968     0.3378   7.687  1.7e-13 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 15.75 on 334 degrees of freedom
Multiple R-squared:  0.1503,    Adjusted R-squared:  0.1478 
F-statistic: 59.09 on 1 and 334 DF,  p-value: 1.697e-13

The correlation between VR anxiety and side effect severity shows \(0.388\), indicating a low positive correlation. Figure 1 supports a linear relationship between two variables. Figure 2 reinforces the relationship by showing approximately random and the same variances in the vertical direction along the fitted axis (homoscedasticity). In conclusion, a linear model between those two variables is appropriate.

Given that the assumptions of the linear model are satisfied, performing a regression test is appropriate. The regression results in the summary statistics show a p-value of \(1.7 \times 10^{-13}\). Assuming a significance level of \(0.05\), we reject the null hypothesis (\(H_0: \text{slope} = 0\)), indicating that the alternative hypothesis (\(H_1: \text{slope} \neq 0\)) is supported. This suggests a significant linear relationship between VR anxiety and side effect severity. The relationship is described by the following regression equation:

\[Side Effect Severity = 2.5968 * (VR Anxiety) + 3.7997\]

Code
ggplot(pooled_melt, aes(x = Label, y = ssq_full, fill = Binary)) +
  geom_boxplot(position = position_dodge(width = 0.8), width = 0.6, outlier.shape = NA) +
  scale_fill_manual(values = c("Positive" = "#377eb8", "Negative" = "#e41a1c")) +
  labs(title = "Distribution of Side Effect Severity Scores by PSTAI Group",
       x = "PSTAI Variable",
       y = "Side Effect Severity",
       fill = "PSTAI Group") +
  theme_minimal()

The above comparative bar plot showed that side effect severity aligned with anxiety, as indicated by higher reports of tension and worry and lower reports of relaxation and calmness, consistent with established markers of anxiety (American Psychiatric Association, 2022).

This is evidenced by the median scores of the side effect severity, where it reveals higher median “Negative” scores for Calm and Relaxed, and higher median “Positive” scores for Tension and Worry.

Code
p <- ggplot(pooled_data, aes(x = expect_group, y = ssq_full, fill = expect_group)) +
  geom_boxplot() +
  labs(
    title = "Side Effect Severity by Expectancy Group",
    x = "Expectancy Group",
    y = "Side Effect Severity"
  ) +
  theme(
    plot.title = element_text(hjust = 0.5)
  )

interactive_plot <- ggplotly(p)
interactive_plot

When looking at the box plot above, the “High” expectancy group median generally shows higher severity outcomes compared to the “Low” expectancy group median. The “High” group also has a wider IQR, pointing to greater variation in symptom severity among those with higher expectancy. Expectancy was divided by whether scores were above or below the overall median. One noticeable feature is an outlier in the Low group, which may explain why the difference between the two groups was less clear in the first analysis.

Taken together, these findings suggest that higher expectancy is associated with stronger VR symptom reporting and greater variability, possibly reflecting increased anxiety in those with high or negative expectancy (Steinman et al., 2013), leading to more severe side effects.

Ethics & Standards

We adhere to the shared professional values of “Truthfulness and Integrity” in our report by demonstrating transparency in the statistical methodologies used and making these methodologies publicly available. We also adhere to “Pursuing Objectivity” by clearly stating the limitations of our findings and employing methods to produce the best possible results, while maintaining open, complete, and transparent outcomes.

Acknowledgments

Group Meetings

Date Time Attendance
Friday, 12 September 2025 18:30 - 20:30
  • Kenneth Davis

  • Edward Danar Atmojo

  • Rickey Arvidson

  • Leo Trimble

  • Yuexi Luo

Friday, 19 September 2025 18:00 - 19:00
  • Kenneth Davis

  • Rickey Arvidson

  • Leo Trimble

  • Yuexi Luo

Contributions

Group Member Contribution
Kenneth Davis Analysis (VR Anxiety and Side Effect Severity Relationship)
Edward Danar Atmojo Analysis (PSTAI and Side Effect Severity Relationship)
Rickey Analysis (Expectancy Score and Side Effect Severity Relationship)
Leo Executive Summary
Cici Luo Exploratory Data Analysis

References

Resources

  • Code Chunks. (n.d.). Rmarkdown.rstudio.com. https://rmarkdown.rstudio.com/lesson-3.html

  • Title Blocks – Quarto. (2018). Quarto. https://quarto.org/docs/authoring/title-blocks.html

  • Wickham, H. (2016b). Dodge overlapping objects side-to-side - position_dodge. - position_dodge • ggplot2. https://ggplot2.tidyverse.org/reference/position_dodge.html

  • R Core Team. (2025). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

  • Melt: Convert an object into a molten data frame. RDocumentation. (2025). https://www.rdocumentation.org/packages/reshape2/versions/1.4.4/topics/melt

  • Wickham, Hadley, Chang, W., Henry, L., Pedersen, T. L., Takahashi, K., Wilke, C., Woo, K., Yutani, H., Dunnington, D., & van den Brand, T. (2025). A box and whiskers plot (in the style of Tukey) - geom_boxplot. - geom_boxplot • ggplot2. https://ggplot2.tidyverse.org/reference/geom_boxplot.html

  • Quarto - HTML Basics. (n.d.). Quarto.org. https://quarto.org/docs/output-formats/html-basics.html

  • The logic of data transformation using melt was suggested during drop in session (Han, Wednesday Drop In Session Tutor , September 17, 2025).

Articles

  • American Psychiatric Association. (2022). Diagnostic and statistical manual of mental disorders (5th ed., text rev.). Arlington, VA: American Psychiatric Publishing.

  • Steinman, S. A., Smyth, F. L., Bucks, R. S., MacLeod, C., & Teachman, B. A. (2013). Anxiety-linked expectancy bias across the adult lifespan. Cognition & Emotion, 27(2), 345–355. https://doi.org/10.1080/02699931.2012.711743

  • Centers for Disease Control and Prevention. (2024, June 25). Symptoms of COVID-19. COVID-19; CDC. https://www.cdc.gov/covid/signs-symptoms/index.html

  • Saunders, C., Colagiuri, B., & Barnes, K. (2023). Socially acquired nocebo effects generalize but are not attenuated by choice. Annals of Behavioral Medicine, 57(12), 1069–1080.https://doi.org/10.1093/abm/kaad056↩︎

AI Usage Statement

No AI was used to inform the report or code.